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All Journal Jurnal ICT : Information and Communication Technologies SEMINAR TEKNOLOGI MAJALENGKA (STIMA)
Permana, Nana Yudi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Transportation

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Use of Differential Evolution Algorithm for Parameter Optimization in Weather Prediction Models Permana, Nana Yudi; Sari, Deassy Ratna Juwita
Jurnal ICT : Information and Communication Technologies Vol. 14 No. 2 (2023): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v14i2.137

Abstract

This research aims to optimize the parameters in a weather prediction model using the Differential Evolution (DE) algorithm, with a focus on improving the accuracy of more reliable weather predictions. The main problems faced in developing weather prediction models are model complexity and uncertainty in parameterization. The DE method is used to adjust the complex parameters in the model, resulting in a significant improvement in weather prediction accuracy based on evaluation using observational data. The implications of this research are that it makes a valuable contribution to our understanding of parameter optimization in weather prediction, as well as improving our ability to predict atmospheric conditions more accurately and reliably.
Optimization of K-Means Algorithm for Big Data Clustering Using Computational Distribution Approach Sari, Deassy Ratna Juwita; Permana, Nana Yudi
Jurnal ICT : Information and Communication Technologies Vol. 14 No. 2 (2023): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v14i2.138

Abstract

In the growing digital era, big data clustering becomes a major challenge in data analysis, especially with the well-known K-Means Algorithm that has limitations in dealing with large-scale data. This study aims to optimize the K-Means Algorithm for big data clustering with a computational distribution approach, to improve clustering efficiency and accuracy. We use the computational distribution approach to process data in parallel across multiple computing nodes, optimize memory usage, develop an intelligent cluster center selection algorithm, and optimize communication between nodes. The implementation of this optimization method successfully improves the efficiency and accuracy of big data clustering, reduces execution time and memory consumption. The practical implications include better business decision making and more effective marketing strategies based on more precise customer data analysis.
PERANCANGAN SISTEM MONITORING AIR KOLAM BERBASIS ANDROID MENGGUNAKAN METODE EXTEREME PROGRAMMING Maulana , Haisyam; Nurlina, Tita; Permana, Nana Yudi
SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Vol 9 (2025): Seminar Teknologi Majalengka (STIMA) 9.0 Tahun 2025
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Tilapia cultivation in Kampung Nila Kawali relies heavily on air quality as a key success factor. Currently, air quality monitoring is still done manually, resulting in inaccurate results, especially with a large number of ponds and extensive cultivation areas. This study aims to design an integrated pond quality monitoring and maintenance system tailored to user needs. The system was developed using the Extreme Programming (XP) method, with stages of planning, design, coding, and testing. This method was chosen because it is flexible to changing needs, accelerates development, and improves communication between developers and users. The design results indicate that the system successfully meets user needs in terms of interface and features. The system can display information on air quality parameters, pond locations, maintenance schedules, and air quality history in a concise and easy-to-understand manner. A warning notification feature is also designed to provide immediate information when air quality parameters are outside normal limits or require maintenance action.
Co-Authors Maulana , Haisyam Nurlina, Tita Sari, Deassy Ratna Juwita